Persistent primary insomnia (PPI) is a prevalent and often debilitating form of sleep difficulty that places significant financial burden on both its sufferers and society at large. Central to this condition are bitter complaints of enduring and significant sleep disturbances. Despite such complaints, researchers' efforts to document these sleep difficulties through the use of conventionally scored polysomnograms (PSG - sleep recordings) have generally yielded findings that are clinically unimpressive. Such results have spawned theories that de-emphasize the clinical significance of PPI-associated sleep difficulties and attribute PPI complaints to such factors as endogenous arousal or subsyndromal mood disturbances. However, previous research and our own pilot work suggest that PSG measures including sleep time, wake time, and sleep stage distribution derived from conventional categorical hand-scoring methods may adequately convey the significant sleep difficulties of only a portion of all PPI sufferers. For the remainder, these conventional measures convey little or no sleep disturbance and imply the presenting PPI complaints represent subjective insomnia or sleep state misperception. Our findings suggest this "blame-the-patient" interpretation overlooks sleep pathology that is better conveyed by sleep EEG spectral amplitude indices than by conventional PSG parameters. In the project proposed, we will test these EEG spectral indices as important neurophysiological markers of sleep disturbance among so-called subjective PPI suffers. To do so we will use a large dataset that includes over 1100 nocturnal PSGs, concomitant subjective sleep estimates (sleep logs), and daytime sleepiness/reaction time test results acquired from 93 PPI sufferers and 102 normal sleepers. To address our Specific Aims, we first will re-score and deriving sleep EEG spectral amplitude measures from all sleep recordings in our database. We then will then apply literature-based criteria to values of conventional sleep measures in order discriminate so-called subjective (i.e., those with seemingly normal PSGs) from objective PPI subtypes. Subsequently we will conduct a number of multivariate statistical tests to determine if sleep EEG spectral measures: (1) discriminate the subjective PPI sufferers from both the objective PPI and normal sleeper groups; (2) perform better than conventional PSG measures as predictors of subjective (sleep log) sleep estimates, daytime sleepiness, and diurnal reaction time performances for the subjective PPI group.